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  <h1>Source code for nlp_architect.utils.generic</h1><div class="highlight"><pre>
<span></span><span class="c1"># ******************************************************************************</span>
<span class="c1"># Copyright 2017-2018 Intel Corporation</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1"># ******************************************************************************</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">unicode_literals</span>

<span class="kn">import</span> <span class="nn">re</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>


<span class="c1"># pylint: disable=invalid-unary-operand-type</span>
<div class="viewcode-block" id="pad_sentences"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.generic.pad_sentences">[docs]</a><span class="k">def</span> <span class="nf">pad_sentences</span><span class="p">(</span>
    <span class="n">sequences</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">max_length</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">padding_value</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">padding_style</span><span class="o">=</span><span class="s2">&quot;post&quot;</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Pad input sequences up to max_length</span>
<span class="sd">    values are aligned to the right</span>

<span class="sd">    Args:</span>
<span class="sd">        sequences (iter): a 2D matrix (np.array) to pad</span>
<span class="sd">        max_length (int, optional): max length of resulting sequences</span>
<span class="sd">        padding_value (int, optional): padding value</span>
<span class="sd">        padding_style (str, optional): add padding values as prefix (use with &#39;pre&#39;)</span>
<span class="sd">            or postfix (use with &#39;post&#39;)</span>

<span class="sd">    Returns:</span>
<span class="sd">        input sequences padded to size &#39;max_length&#39;</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sequences</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">sequences</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="n">sequences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">sequences</span><span class="p">)</span>
        <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;cannot convert sequences into numpy array&quot;</span><span class="p">)</span>
    <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">sequences</span><span class="p">,</span> <span class="s2">&quot;shape&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sequences</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">sequences</span>
    <span class="k">if</span> <span class="n">max_length</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">max_length</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sequences</span><span class="p">])</span>
    <span class="k">elif</span> <span class="n">max_length</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;max sequence length must be &gt; 0&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">max_length</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">sequences</span>
    <span class="n">padded_sequences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">sequences</span><span class="p">),</span> <span class="n">max_length</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> <span class="o">*</span> <span class="n">padding_value</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sent</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sequences</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">padding_style</span> <span class="o">==</span> <span class="s2">&quot;post&quot;</span><span class="p">:</span>
            <span class="n">trunc</span> <span class="o">=</span> <span class="n">sent</span><span class="p">[</span><span class="o">-</span><span class="n">max_length</span><span class="p">:]</span>
            <span class="n">padded_sequences</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">trunc</span><span class="p">)]</span> <span class="o">=</span> <span class="n">trunc</span>
        <span class="k">elif</span> <span class="n">padding_style</span> <span class="o">==</span> <span class="s2">&quot;pre&quot;</span><span class="p">:</span>
            <span class="n">trunc</span> <span class="o">=</span> <span class="n">sent</span><span class="p">[:</span><span class="n">max_length</span><span class="p">]</span>
            <span class="n">padded_sequences</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="n">trunc</span><span class="p">:]</span> <span class="o">=</span> <span class="n">trunc</span>
    <span class="k">return</span> <span class="n">padded_sequences</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span></div>


<div class="viewcode-block" id="one_hot"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.generic.one_hot">[docs]</a><span class="k">def</span> <span class="nf">one_hot</span><span class="p">(</span><span class="n">mat</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Convert a 1D matrix of ints into one-hot encoded vectors.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        mat (numpy.ndarray): A 1D matrix of labels (int)</span>
<span class="sd">        num_classes (int): Number of all possible classes</span>

<span class="sd">    Returns:</span>
<span class="sd">        numpy.ndarray: A 2D matrix</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">mat</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">2</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span>
    <span class="n">vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">num_classes</span><span class="p">))</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">mat</span><span class="p">):</span>
        <span class="n">vec</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
    <span class="k">return</span> <span class="n">vec</span></div>


<div class="viewcode-block" id="one_hot_sentence"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.generic.one_hot_sentence">[docs]</a><span class="k">def</span> <span class="nf">one_hot_sentence</span><span class="p">(</span><span class="n">mat</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Convert a 2D matrix of ints into one-hot encoded 3D matrix</span>

<span class="sd">    Arguments:</span>
<span class="sd">        mat (numpy.ndarray): A 2D matrix of labels (int)</span>
<span class="sd">        num_classes (int): Number of all possible classes</span>

<span class="sd">    Returns:</span>
<span class="sd">        numpy.ndarray: A 3D matrix</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">new_mat</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
        <span class="n">new_mat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">one_hot</span><span class="p">(</span><span class="n">mat</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">num_classes</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">new_mat</span><span class="p">)</span></div>


<div class="viewcode-block" id="add_offset"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.generic.add_offset">[docs]</a><span class="k">def</span> <span class="nf">add_offset</span><span class="p">(</span><span class="n">mat</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">offset</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Add +1 to all values in matrix mat</span>

<span class="sd">    Arguments:</span>
<span class="sd">        mat (numpy.ndarray): A 2D matrix with int values</span>
<span class="sd">        offset (int): offset to add</span>

<span class="sd">    Returns:</span>
<span class="sd">        numpy.ndarray: input matrix</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">vec</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">mat</span><span class="p">):</span>
        <span class="n">offset_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">vec</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
        <span class="n">offset_arr</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">offset</span><span class="p">)</span>
        <span class="n">mat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">vec</span> <span class="o">+</span> <span class="n">offset_arr</span>
    <span class="k">return</span> <span class="n">mat</span></div>


<div class="viewcode-block" id="license_prompt"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.generic.license_prompt">[docs]</a><span class="k">def</span> <span class="nf">license_prompt</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">model_website</span><span class="p">,</span> <span class="n">dataset_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">dataset_dir</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">***</span><span class="se">\n</span><span class="si">{}</span><span class="s2"> was not found in the directory: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">dataset_dir</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">***</span><span class="se">\n\n</span><span class="si">{}</span><span class="s2"> was not found on local installation&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_name</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> can be downloaded from </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">model_website</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span>
        <span class="s2">&quot;The terms and conditions of the data set license apply. Intel does not &quot;</span>
        <span class="s2">&quot;grant any rights to the data files or database</span><span class="se">\n</span><span class="s2">&quot;</span>
    <span class="p">)</span>
    <span class="n">response</span> <span class="o">=</span> <span class="nb">input</span><span class="p">(</span>
        <span class="s2">&quot;To download &#39;</span><span class="si">{}</span><span class="s2">&#39; from </span><span class="si">{}</span><span class="s2">, please enter YES: &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">model_website</span><span class="p">)</span>
    <span class="p">)</span>
    <span class="n">res</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">res</span> <span class="o">==</span> <span class="s2">&quot;yes&quot;</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">res</span> <span class="o">==</span> <span class="s2">&quot;y&quot;</span><span class="p">):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Downloading </span><span class="si">{}</span><span class="s2">...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_name</span><span class="p">))</span>
        <span class="n">responded_yes</span> <span class="o">=</span> <span class="kc">True</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Download declined. Response received </span><span class="si">{}</span><span class="s2"> != YES|Y. &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
        <span class="k">if</span> <span class="n">dataset_dir</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span>
                <span class="s2">&quot;Please download the model manually from </span><span class="si">{}</span><span class="s2"> and place in the directory: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                    <span class="n">model_website</span><span class="p">,</span> <span class="n">dataset_dir</span>
                <span class="p">)</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Please download the model manually from </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_website</span><span class="p">))</span>
        <span class="n">responded_yes</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="k">return</span> <span class="n">responded_yes</span></div>


<span class="c1"># character vocab</span>
<span class="n">zhang_lecun_vocab</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="s2">&quot;abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:’/</span><span class="se">\\</span><span class="s2">|_@#$%ˆ&amp;*˜‘+=&lt;&gt;()[]</span><span class="si">{}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">vocab_hash</span> <span class="o">=</span> <span class="p">{</span><span class="n">b</span><span class="p">:</span> <span class="n">a</span> <span class="k">for</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">zhang_lecun_vocab</span><span class="p">)}</span>


<div class="viewcode-block" id="normalize"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.generic.normalize">[docs]</a><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span>
    <span class="n">txt</span><span class="p">,</span>
    <span class="n">vocab</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
    <span class="n">replace_char</span><span class="o">=</span><span class="s2">&quot; &quot;</span><span class="p">,</span>
    <span class="n">max_length</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
    <span class="n">pad_out</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
    <span class="n">to_lower</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
    <span class="n">reverse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
    <span class="n">truncate_left</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
    <span class="n">encoding</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>

    <span class="c1"># remove html</span>
    <span class="c1"># This will keep characters and other symbols</span>
    <span class="n">txt</span> <span class="o">=</span> <span class="n">txt</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
    <span class="c1"># Remove HTML</span>
    <span class="n">txt</span> <span class="o">=</span> <span class="p">[</span><span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;http:.*&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">txt</span><span class="p">]</span>
    <span class="n">txt</span> <span class="o">=</span> <span class="p">[</span><span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;https:.*&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">txt</span><span class="p">]</span>

    <span class="n">txt</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">txt</span><span class="p">)</span>

    <span class="c1"># Remove punctuation</span>
    <span class="n">txt</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">&quot;[.,!]&quot;</span><span class="p">,</span> <span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="n">txt</span><span class="p">)</span>
    <span class="n">txt</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">txt</span><span class="o">.</span><span class="n">split</span><span class="p">())</span>

    <span class="c1"># store length for multiple comparisons</span>
    <span class="n">txt_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">txt</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">truncate_left</span><span class="p">:</span>
        <span class="n">txt</span> <span class="o">=</span> <span class="n">txt</span><span class="p">[</span><span class="o">-</span><span class="n">max_length</span><span class="p">:]</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">txt</span> <span class="o">=</span> <span class="n">txt</span><span class="p">[:</span><span class="n">max_length</span><span class="p">]</span>
    <span class="c1"># change case</span>
    <span class="k">if</span> <span class="n">to_lower</span><span class="p">:</span>
        <span class="n">txt</span> <span class="o">=</span> <span class="n">txt</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
    <span class="c1"># Reverse order</span>
    <span class="k">if</span> <span class="n">reverse</span><span class="p">:</span>
        <span class="n">txt</span> <span class="o">=</span> <span class="n">txt</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1"># replace chars</span>
    <span class="k">if</span> <span class="n">vocab</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">txt</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">c</span> <span class="k">if</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">vocab</span> <span class="k">else</span> <span class="n">replace_char</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">txt</span><span class="p">])</span>
    <span class="c1"># re-encode text</span>
    <span class="k">if</span> <span class="n">encoding</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">txt</span> <span class="o">=</span> <span class="n">txt</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">encoding</span><span class="p">,</span> <span class="n">errors</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
    <span class="c1"># pad out if needed</span>
    <span class="k">if</span> <span class="n">pad_out</span> <span class="ow">and</span> <span class="n">max_length</span> <span class="o">&gt;</span> <span class="n">txt_len</span><span class="p">:</span>
        <span class="n">txt</span> <span class="o">=</span> <span class="n">txt</span> <span class="o">+</span> <span class="n">replace_char</span> <span class="o">*</span> <span class="p">(</span><span class="n">max_length</span> <span class="o">-</span> <span class="n">txt_len</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">txt</span></div>


<div class="viewcode-block" id="to_one_hot"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.generic.to_one_hot">[docs]</a><span class="k">def</span> <span class="nf">to_one_hot</span><span class="p">(</span><span class="n">txt</span><span class="p">,</span> <span class="n">vocab</span><span class="o">=</span><span class="n">vocab_hash</span><span class="p">):</span>
    <span class="n">vocab_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">vocab</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
    <span class="n">one_hot_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">vocab_size</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">txt</span><span class="p">)),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
    <span class="c1"># run through txt and &quot;switch on&quot; relevant positions in one-hot vector</span>
    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">char</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">txt</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">char</span> <span class="ow">in</span> <span class="n">vocab_hash</span><span class="p">:</span>
            <span class="n">vocab_idx</span> <span class="o">=</span> <span class="n">vocab_hash</span><span class="p">[</span><span class="n">char</span><span class="p">]</span>
            <span class="n">one_hot_vec</span><span class="p">[</span><span class="n">vocab_idx</span><span class="p">,</span> <span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
        <span class="c1"># raised if character is out of vocabulary</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">pass</span>
    <span class="k">return</span> <span class="n">one_hot_vec</span></div>


<div class="viewcode-block" id="balance"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.generic.balance">[docs]</a><span class="k">def</span> <span class="nf">balance</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Balancing the classes&quot;</span><span class="p">)</span>
    <span class="n">type_counts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Sentiment&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
    <span class="n">min_count</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">type_counts</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>

    <span class="n">balanced_df</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">type_counts</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">df_sub</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Sentiment&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="n">min_count</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">balanced_df</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">balanced_df</span> <span class="o">=</span> <span class="n">balanced_df</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_sub</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">balanced_df</span> <span class="o">=</span> <span class="n">df_sub</span>
    <span class="k">return</span> <span class="n">balanced_df</span></div>
</pre></div>

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